As an Agentic AI Software Engineer, you will own the design and development of intelligent systems that reason over real-time network telemetry, automate operational decisions, and surface actionable insights to cluster operators. You will integrate frontier LLM APIs alongside purpose-trained smaller models fine-tuned for networking-specific tasks to enable autonomous network management. You will solve hard problems at the intersection of LLM orchestration, retrieval-augmented generation, and low-latency infrastructure. This role spans architecture through production deployment across Aria’s cluster software stack.

Responsibilities

• Design and build agentic workflows like tool use, multi-step reasoning, and RAG pipelines that operate over fine-grained GPU-cluster network telemetry
• Integrate LLM APIs (OpenAI, Anthropic, Google or equivalent) into production systems with reliable orchestration, structured outputs, and robust failure handling
• Fine-tune and distill smaller task-specific models (LoRA, PEFT, or equivalent) for networking-domain tasks including anomaly detection, fault classification, and predictive analytics
• Architect the AI layer of Aria Cluster Software, defining how agentic components interact with the telemetry pipeline, control plane, and operator-facing interfaces
• Define evaluation frameworks to measure AI system accuracy, latency, and decision quality against real network operational outcomes
• Partner with telemetry and backend engineers to shape data schemas and streaming infrastructure that serve AI workloads efficiently
• Ship AI capabilities end to end with appropriate observability, rollback mechanisms, and confidence calibration

Qualifications

• Bachelor’s degree (or foreign equivalent) in Computer Science, Software Engineering, Computer Engineering, or a closely related technical field
• Minimum 5 years of software engineering experience, with at least 2 years in hands-on AI/ML product development
• Experience designing and implementing agentic workflows including multi-step reasoning, retrieval-augmented generation, and dynamic tool invocation
• Experience fine-tuning or training smaller models using LoRA, PEFT, distillation, or equivalent techniques
• Strong Python engineering fundamentals like clean abstractions, testable code, production-grade systems
• Working knowledge of ML frameworks such as PyTorch, JAX, Tensorflow, etc.
• Experience building AI-powered control or automation systems that act on live system state is preferred
• Experience applying AI/ML to networking, infrastructure, or observability domains is a significant plus